SoroushVahidi/scidocs huggingface dataset:
SELF-SERV: A Platform for Rapid Composition of Web Services in a Peer-to-Peer Environment
(2002)

The automation of Web services interoperation is gaining a considerable momentum as a paradigm for effective Business-to-Business collaboration [2]. Established enterprises are continuously discovering new opportunities to form alliances with other enterprises, by offering value-added integrated services. However, the technology to compose Web services in appropriate time-frames has not kept pace with the rapid growth and volatility of available opportunities. Indeed, the development of integrated Web services is often ad-hoc and requires a considerable effort of lowlevel programming. This approach is inadequate given the size and the volatility of the Web. Furthermore, the number of services to be integrated may be large, so that approaches where the development of an integrated service requires the understanding of each of the underlying services are inappropriate. In addition, Web services may need to be composed as part of a short term partnership, and disbanded when the partnership is no longer profitable. Hence, the integration of a large number of Web services requires scalable and flexible techniques, such as those based on declarative languages. Also, the execution of an integrated service in existing approaches is usually centralised, whereas the underlying services are distributed and autonomous. This calls for the investigation of distributed execution paradigms (e.g., peer-to-peer models), that do not suffer of the scalability and availability problems of centralised coordination [3]. Motivated by these concerns, we have developed the SELF-SERV platform for rapid composition of Web
Metal nanogrids , nanowires , and nanofi bers for transparent electrodes
(2011)

VOLUME 36 • OCTOBER 2011 • www.mrs.org/bulletin © 2011 Materials Research Society Introduction Transparent electrodes are ubiquitously used in optoelectronic devices where light must pass through a section, and current or voltage needs to be applied. These devices include thin-fi lm solar cells (organic, inorganic, hybrid), displays (liquid-crystal display, plasma display, organic light-emitting diode [OLED]), touch screens (resistive, capacitive), electronic books, smart windows, fl exible lighting, and passive devices (such as transparent heating, transparent speakers). 1 The properties of transparent electrodes are extremely important for device performance, and the specifi cations vary based on types of devices. For example, while source roughness and work function are insignifi cant for resistive touch screens, they are crucial for OLEDs and organic solar cells. 1 Moreover, the optical haze of particular transparent electrodes is benefi cial for high-effi ciency solar cells but problematic for certain types of displays. The mechanical properties of the electrode are crucial for fl exible devices but not important for devices on rigid substrates. Across all devices, however, the optical transparency ( T ) and electrical sheet resistance ( Rs ) are the two most important parameters. Unfortunately, these two parameters are in constant competition with one another. Since Rs values are often reported at different transparencies, it is hard to compare two transparent electrodes directly, and hence a fi gure of merit ( σ dc / σ op , where σ dc is the dc conductivity, and σ op is the optical conductivity) Metal nanogrids, nanowires, and nanofi bers for transparent electrodes
Microwave-Based Noninvasive Concentration Measurements for Biomedical Applications
(2013)

A Debye relaxation model for the permittivity of aqueous glucose solution is given up to 40 GHz. Measurements of aqueous solutions are compared with invasive measurements on over 40 blood samples. Reflection- and transmission-based techniques are explained, concerning the application in biomedical concentration measurements. Wideband measurements on different sensing structures are introduced. Reflection- and transmission-based sensors are applied to a six-port reflectometer, as well as a homodyne vector network analyzer.
Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol.
(2015)

INTRODUCTION
Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting.


METHODS AND ANALYSIS
We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results.


ETHICS AND DISSEMINATION
This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.
Passivity of a class of sampled-data systems: Application to haptic interfaces
(1997)

Passivity of systems comprising a continuous time plant and discrete time controller is considered. This topic is motivated by stability considerations arising in the control of robots and force-reflecting human interfaces (“haptic interfaces”). A necessary and sufficient condition for the passivity of a class of sampled-data systems is derived. An example — implementation of a “virtual wall” via a one degree-of-freedom haptic interface — is presented.
Mapping high dimensional features onto Hilbert curve: Applying to fast image retrieval
(2012)

The use of high-dimension features is unavoidable in many applications of image retrieval and techniques of dimension reductions are not always efficient. The space-filling curve reduces the number of dimensions to one while preserving the neighborhood relation. In this paper, Hilbert curve, the most neighborhood preserving space-filling curve, is used in shape retrieval. The retrieving is accelerated by ordering the shapes in 1-D dynamical data structure, which enables rapid insertions of new images without changing existing data. A proposal of fast mapping facilitate the computing of 1-D Hilbert indexes from high dimensional features.
An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores
(2011)

This paper presents a novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation. The non-uniform brightness of the FKP due to relatively curvature surface is corrected and texture is enhanced. The local features of the enhanced FKP are extracted using the scale invariant feature transform (SIFT) and the speeded up robust features (SURF). Corresponding features of the enrolled and the query FKPs are matched using nearest-neighbour-ratio method and then the derived SIFT and SURF matching scores are fused using weighted sum rule. The proposed system is evaluated using PolyU FKP database of 7920 images for both identification mode and verification mode. It is observed that the system performs with CRR of 100% and EER of 0.215%. Further, it is evaluated against various scales and rotations of the query image and is found to be robust for query images downscaled upto 60% and for any orientation of query image.
Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph
(2011)

There is high demand for automated tools that assign polarity to microblog content such as tweets (Twitter posts), but this is challenging due to the terseness and informality of tweets in addition to the wide variety and rapid evolution of language in Twitter. It is thus impractical to use standard supervised machine learning techniques dependent on annotated training examples. We do without such annotations by using label propagation to incorporate labels from a maximum entropy classifier trained on noisy labels and knowledge about word types encoded in a lexicon, in combination with the Twitter follower graph. Results on polarity classification for several datasets show that our label propagation approach rivals a model supervised with in-domain annotated tweets, and it outperforms the noisily supervised classifier it exploits as well as a lexicon-based polarity ratio classifier.
Learning Visual Similarity Measures for Comparing Never Seen Objects
(2007)

In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled "same" or "different". This is far less informative than the commonly used individual image labels (e.g., "car model X"), but it is cheaper to obtain. The proposed algorithm learns the characteristic differences between local descriptors sampled from pairs of "same" and "different" images. These differences are vector quantized by an ensemble of extremely randomized binary trees, and the similarity measure is computed from the quantized differences. The extremely randomized trees are fast to learn, robust due to the redundant information they carry and they have been proved to be very good clusterers. Furthermore, the trees efficiently combine different feature types (SIFT and geometry). We evaluate our innovative similarity measure on four very different datasets and consistently outperform the state-of-the-art competitive approaches.
Ecological momentary assessment.
(2008)

Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.
Social media for the emergency manager in disaster planning and response
(2014)

This practitioner paper outlines some of the benefits for the use of social media, from the perspective of a locallevel or county-level emergency manager (EM). As compared to state and national level emergency management, because local level EMs have limited manpower and resources, social media can positively or negatively impact the effectiveness of communication before, during and after disaster strikes. Outlined in this paper are six key points where local EMs have specific needs that could be addressed by the effective use of social media and, in the opinion of the authors, represent the top issues that EMs face when considering how to leverage Twitter, Facebook, YouTube, Instagram and other social media platforms. The six needs addressed in this paper include: 1) Best practices for general social media use by EMs, 2) Social media use for internal command and control within the EM group, 3) Developing situation awareness by monitoring social media, especially prior to predicable events, 4) Communicating disaster preparedness messages through social media, 5) Using social media for gathering damage assessment information during, or immediately following a crisis, and 6) Leveraging social media volunteer groups. This short paper picks up where the Federal Emergency Management Agency’s social media training leaves off and attempts to represent these six needs as use cases for researchers and developers to address in future publications and products.
On battery State of Charge estimation: A new mixed algorithm
(2008)

A new SoC estimation algorithm is presented. The estimation is performed mixing the Coulomb-Counting and the Model-Based estimation approaches. The algorithm was tested on a brand new lithium-ion cell which use a nanoscale technology able to grant a more powerful and safer lithium-ion battery than the batteries currently on the market. The cell, called A123-M1, was first characterized and then identified. The cell was identified with a equivalent electrical circuits model which is usually called 2th Randle model: the used test-bench and the identification process are described. Then the SoC estimation problem is described. A briefly review of the state of art of SoC estimation is given and the drawbacks of the methods which are usually used are presented. Finally the new mixed estimation algorithm is described: the algorithm scheme and the results are shown.
A modified two-section UWB Wilkinson power divider
(2008)

A novel Wilkinson power divider with modification of the traditional two-section Wilkinson power divider by adding one open stub on each branch is proposed. This design method is validated by building a divider working in the UWB band (3.1 GHz - 10.6 GHz). It is better in performance, but is about 10% smaller than the traditional three-section power divider and spares one resistor. The simulation and measurement results of the modified divider show equal power split, a good insertion loss of less than 1.1 dB, a good return loss of all ports of less than - 10 dB, and a good isolation is less than -11 dB over the UWB band.
Online EM for Unsupervised Models
(2009)

The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better solutions than those found by batch EM. We support these findings on four unsupervised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.
Semantic annotation in historical documents
(2017)

This paper presents an approach to proceed with semantic annotation in historical documents from the 19th century that discuss the constitution of the mother language, the Portuguese Language in Brazil. The objective is to generate a group of semantically annotated documents in agreement with a domain ontology. To provide this domain ontology, the Linguistic Instrument Ontology was employed, and it supported the process of automatic semantic annotation. The results obtained with the annotation were analyzed in comparison with the Gold Standard and they presented an elevated level of coincidence, between 0.86 and 1.00 for the F1-score measure. Besides that, it was possible to locate new documents about the discussed domain in a sample of the Revistas Brazileiras. These results prove the efficacy of the approach of automatic semantic annotation.
Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task
(2017)

This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).
Active current balancing for parallel-connected silicon carbide MOSFETs
(2013)

In high power applications of silicon carbide (SiC) MOSFETs where parallelism is employed, current unbalance can occur and affect the performance and reliability of the power devices. In this paper, factors which cause current unbalance in these devices are analyzed. Among them, the threshold voltage mismatch is identified as a major factor for dynamic current unbalance. The threshold distribution of SiC MOSFETs is investigated, and its effect on current balance is studied in experiments. Based on these analyses, an active current balancing scheme is proposed. It is able to sense the unbalanced current and eliminate it by actively controlling the gate drive signal to each device. The features of fine time resolution and low complexity make this scheme attractive to a wide variety of wide-band-gap device applications. Experimental and simulation results verify the feasibility and effectiveness of the proposed scheme.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
(2018)

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, CURB, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
(2009)

Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.
A Survey of Collaborative Filtering Techniques
(2009)

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Features for Measuring Credibility on Facebook Information
(2015)

Abstract—Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.
Neural Kinematic Networks for Unsupervised Motion Retargetting
(2018)

We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.